"""Execution-recovery matrix: is the close→next_open gap recoverable by scheduling?

Hypothesis (from Phase A A4)
----------------------------
The 1.77 → 1.20 full-period Sharpe gap under next_open fill is mostly overnight
momentum drift that a 07:00-Berlin scanner cannot earn. Near-close / MOC-style
execution (scan ~15:45 ET, fill at/near that close) should recover it.

Pre-registered arms (N for DSR = 4)
----------------------------------
1. ``close_control`` — historical optimistic control (signal = fill at same close).
2. ``next_open`` — honest overnight scanner (decision baseline for future promotion).
3. ``stale_close`` — signal at t−1 close, fill at t close (one-session-stale MOC proxy).
   Expectation: ≈ close_control; if so, the gap is scheduling, not physics.
4. ``next_open_gap2`` — next_open but skip entries that open > +2% above signal close.
   Measure whether large gap-ups are toxic or the best continuations.

Promotion / read rule (pre-registered)
--------------------------------------
- Decision baseline for *future* strategy work: ``next_open``.
- Recovery success for ``stale_close``: validation Sharpe within 0.5×SE of
  ``close_control`` **and** validation Sharpe ≥ ``next_open``; train Sharpe not
  worse than close_control by more than 0.5×SE. State 1-SE distinguishability.
- ``next_open_gap2`` is measurement-only vs ``next_open`` (no auto-promote to live).

Reuses the same daily candidate cache as the Phase A matrix when the cache key
matches.

Usage
-----
  python scripts/run_execution_recovery_matrix.py backtest_snapshots/prod.sqlite \\
      --workers 7 --allow-spawn \\
      --candidate-cache reports/.cache/research-cands.pkl \\
      --out reports/execution-recovery-matrix.json
"""

from __future__ import annotations

import argparse
import asyncio
import json
import multiprocessing
import os
import pickle
import sys
from concurrent.futures import ProcessPoolExecutor, as_completed
from datetime import date, datetime
from pathlib import Path
from typing import Any

from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine

ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from scripts.research_rankings import (  # noqa: E402
    _live_universe_rank_map,
    _period_percentiles,
)

# Must match Phase A cache when reusing research-cands.pkl
CACHE_VERSION = "research-matrix-v1-daily-prod"

PRE_REGISTERED_ARMS: tuple[dict[str, Any], ...] = (
    {
        "id": "close_control",
        "label": "Close fill (historical control)",
        "fill_mode": "close",
    },
    {
        "id": "next_open",
        "label": "Next-open fill (decision baseline)",
        "fill_mode": "next_open",
    },
    {
        "id": "stale_close",
        "label": "Stale-signal close fill (MOC proxy: signal t-1, fill t close)",
        "fill_mode": "stale_close",
    },
    {
        "id": "next_open_gap2",
        "label": "Next-open + skip gap-up > 2%",
        "fill_mode": "next_open",
        "max_entry_gap_pct": 0.02,
    },
)
N_TRIALS = len(PRE_REGISTERED_ARMS)


def _sqlite_url(path: Path) -> str:
    return f"sqlite+aiosqlite:///{path.resolve().as_posix()}"


def _parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(
        description=__doc__,
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    p.add_argument("snapshot")
    p.add_argument("--workers", type=int, default=6)
    p.add_argument("--allow-spawn", action="store_true")
    p.add_argument("--out", default=None)
    p.add_argument("--candidate-cache", default=None)
    p.add_argument("--validation-split", default="2024-07-01")
    p.add_argument("--cadence", choices=("daily", "weekly"), default="daily")
    p.add_argument("--quiet", action="store_true")
    return p.parse_args()


def _window(arm: dict, name: str) -> dict | None:
    for row in arm.get("windows") or []:
        if row.get("window") == name:
            return row
    return None


def _grade_stale_close(close_arm: dict, next_arm: dict, stale_arm: dict) -> dict:
    c_val = _window(close_arm, "validation") or {}
    n_val = _window(next_arm, "validation") or {}
    s_val = _window(stale_arm, "validation") or {}
    c_tr = _window(close_arm, "train") or {}
    s_tr = _window(stale_arm, "train") or {}
    keys = ("sharpe", "sharpe_se")
    if any(c_val.get(k) is None for k in keys) or s_val.get("sharpe") is None:
        return {"recover": False, "reason": "missing Sharpe rows"}
    se = float(c_val.get("sharpe_se") or s_val.get("sharpe_se") or 0.0)
    half_se = 0.5 * se if se > 0 else 0.0
    cs, ss, ns = float(c_val["sharpe"]), float(s_val["sharpe"]), n_val.get("sharpe")
    cts, sts = c_tr.get("sharpe"), s_tr.get("sharpe")
    near_close = abs(ss - cs) <= half_se if half_se > 0 else abs(ss - cs) < 0.05
    beats_next = ns is None or ss >= float(ns)
    train_ok = (
        cts is None
        or sts is None
        or float(sts) >= float(cts) - half_se
    )
    recover = near_close and beats_next and train_ok
    return {
        "recover": recover,
        "near_close_control": near_close,
        "beats_next_open": beats_next,
        "train_ok": train_ok,
        "validation_delta_vs_close": round(ss - cs, 4),
        "validation_delta_vs_next_open": (
            round(ss - float(ns), 4) if ns is not None else None
        ),
        "half_se": half_se,
        "reason": (
            "stale_close recovers close-fill economics (within 0.5 SE) and beats next_open"
            if recover
            else "stale_close does not meet recovery criteria — see flags"
        ),
    }


def _markdown(report: dict) -> str:
    lines = [
        f"# Execution recovery matrix — {report.get('generated_at', '')}",
        "",
        f"Validation split: **{report.get('validation_split')}**. N for DSR: **{report.get('n_trials')}**.",
        "",
        "| arm | window | Sharpe | SE | CAGR | MaxDD | trades | gap/drift |",
        "|---|---|---:|---:|---:|---:|---:|---|",
    ]
    for arm in report.get("arms") or []:
        for w in arm.get("windows") or []:
            slip = w.get("overnight_slippage") or w.get("signal_to_fill_drift") or {}
            slip_s = (
                f"mean {slip.get('mean_pct')}% n={slip.get('n')}"
                if slip
                else "—"
            )
            if w.get("skipped_gap_cap") is not None:
                slip_s += f"; gap_skips={w.get('skipped_gap_cap')}"
            lines.append(
                f"| {arm.get('id')} | {w.get('window')} | {w.get('sharpe')} | "
                f"{w.get('sharpe_se')} | {w.get('cagr_pct')} | {w.get('max_drawdown_pct')} | "
                f"{w.get('trades')} | {slip_s} |"
            )
    rec = report.get("recovery") or {}
    lines.extend(
        [
            "",
            "## Recovery decision (stale_close)",
            "",
            f"- **recover: {rec.get('recover')}** — {rec.get('reason')}",
            f"- flags: { {k: rec.get(k) for k in ('near_close_control', 'beats_next_open', 'train_ok', 'validation_delta_vs_close', 'validation_delta_vs_next_open', 'half_se')} }",
            "",
        ]
    )
    return "\n".join(lines)


def _checkpoint(path: Path, report: dict) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    tmp = path.with_suffix(path.suffix + ".tmp")
    tmp.write_text(json.dumps(report, indent=2, default=str), encoding="utf-8")
    tmp.replace(path)
    path.with_suffix(".md").write_text(_markdown(report), encoding="utf-8")


async def _main() -> None:
    args = _parse_args()
    snapshot = Path(args.snapshot)
    if not snapshot.exists():
        raise SystemExit(f"Snapshot not found: {snapshot}")
    validation_split = date.fromisoformat(args.validation_split)
    out_path = (
        Path(args.out)
        if args.out
        else Path("reports")
        / f"execution-recovery-matrix-{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
    )

    os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1"
    if args.allow_spawn:
        os.environ["BACKTEST_ALLOW_SPAWN"] = "1"

    from app.models.ticker import Ticker
    from app.services import backtest_service as bt
    from app.services.admin_service import get_activation_config
    from app.services.paper_trade_service import get_exit_policy
    from app.services.recommendation_service import get_recommendation_config

    db_engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True)
    Session = async_sessionmaker(db_engine, class_=AsyncSession, expire_on_commit=False)
    try:
        async with Session() as db:
            recommendation_config = await get_recommendation_config(db)
            activation = await get_activation_config(db)
            exit_config = await get_exit_policy(db)
            benchmark_closes = await bt._load_benchmark_closes_for_backtest(
                db, days=None, refresh=False
            )
            ticker_result = await db.execute(select(Ticker).order_by(Ticker.symbol))
            symbols = [t.symbol for t in ticker_result.scalars().all()]
            prices: dict[str, tuple] = {}
            for index, symbol in enumerate(symbols, 1):
                columns = await bt._fetch_columns(db, symbol)
                if columns is not None:
                    prices[symbol] = columns
                if not args.quiet and index % 50 == 0:
                    print(f"loaded prices: {index}/{len(symbols)}", flush=True)
    finally:
        await db_engine.dispose()

    snapshot_stat = snapshot.stat()
    cache_key = {
        "version": CACHE_VERSION,
        "snapshot": str(snapshot.resolve()),
        "snapshot_size": snapshot_stat.st_size,
        "snapshot_mtime_ns": snapshot_stat.st_mtime_ns,
        "cadence": args.cadence,
        "target_model": "production_gtl",
    }
    cache_path = Path(args.candidate_cache) if args.candidate_cache else None
    qualified: list[dict] | None = None
    entry_candidate_count = 0

    if cache_path is not None and cache_path.exists():
        with cache_path.open("rb") as handle:
            cached = pickle.load(handle)  # noqa: S301
        if cached.get("key") == cache_key:
            qualified = list(cached["qualified_candidates"])
            entry_candidate_count = int(cached.get("entry_candidate_count") or 0)
            if not args.quiet:
                print(f"loaded candidate cache: {cache_path}", flush=True)

    if qualified is None:
        workers = max(1, min(int(args.workers), max(1, multiprocessing.cpu_count() - 1)))
        context = bt._mp_context() or multiprocessing.get_context("spawn")
        replay_rows: list[dict] = []
        with ProcessPoolExecutor(max_workers=workers, mp_context=context) as pool:
            futures = {
                pool.submit(
                    bt._replay_candidates_for_period,
                    symbol,
                    columns,
                    recommendation_config,
                    activation,
                    benchmark_closes,
                    date(1900, 1, 1),
                    args.cadence,
                    True,
                    True,
                ): symbol
                for symbol, columns in prices.items()
            }
            for index, future in enumerate(as_completed(futures), 1):
                replay_rows.extend(future.result())
                if not args.quiet and index % 25 == 0:
                    print(f"replay: {index}/{len(futures)}", flush=True)
        setup_candidates = [row for row in replay_rows if not row.get("_rank_only")]
        rank_observations = [
            row for row in replay_rows if row.get("_universe_rank_observation")
        ]
        entry_candidate_count = len(setup_candidates)
        live_ranks = _live_universe_rank_map(
            rank_observations,
            benchmark_closes,
            bt.STRATEGY_RANK_MOMENTUM_WEIGHT,
        )
        threshold = float(activation.get("min_momentum_percentile", 80.0))
        qualified = []
        for setup in setup_candidates:
            if setup.get("direction") != "long":
                continue
            candidate = {
                k: v
                for k, v in setup.items()
                if not k.startswith("_universe_")
            }
            rank = live_ranks.get((str(setup["symbol"]), str(setup["date"])))
            if rank is None:
                continue
            candidate[bt.PRODUCTION_PERCENTILE_KEY] = rank["momentum_percentile"]
            candidate[bt.VOL_PERCENTILE_KEY] = rank["volatility_percentile"]
            candidate[bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY] = rank["strategy_rank"]
            candidate["qualified"] = bt._momentum_qualifies(candidate, threshold)
            if candidate["qualified"]:
                qualified.append(candidate)
        if cache_path is not None:
            cache_path.parent.mkdir(parents=True, exist_ok=True)
            with cache_path.open("wb") as handle:
                pickle.dump(
                    {
                        "key": cache_key,
                        "entry_candidate_count": entry_candidate_count,
                        "qualified_candidates": qualified,
                    },
                    handle,
                    protocol=pickle.HIGHEST_PROTOCOL,
                )

    if not qualified:
        raise SystemExit("No qualified candidates")

    strategy = next(s for s in bt.PORTFOLIO_MONITOR_STRATEGIES if s.get("is_production"))
    entry_config = bt._entry_variant_config(str(strategy["entry_variant"]))
    assert entry_config is not None
    ranking_key = str(entry_config.get("ranking_key") or entry_config["percentile_key"])
    exit_policy = bt.LIVE_EXIT_MODE_TO_SIM.get(
        str(exit_config.get("mode", "atr_trailing")), "atr_trail3"
    )
    hold_days = int(exit_config.get("hold_days", 30))
    trail_multiplier = float(exit_config.get("atr_multiplier", bt.ATR_TRAIL_MULTIPLIER))
    risk_per_trade = float(entry_config["risk_per_trade"])
    max_positions = int(entry_config["max_positions"])
    post_stop = bt._make_gate_reset_reentry_fn(
        qualified, prices, cadence=args.cadence, ranking_key=ranking_key
    )

    report: dict[str, Any] = {
        "generated_at": datetime.now().isoformat(),
        "snapshot": str(snapshot.resolve()),
        "validation_split": validation_split.isoformat(),
        "n_trials": N_TRIALS,
        "hypothesis": (
            "stale_close (signal t-1, fill t close) recovers close-fill economics; "
            "the next_open haircut is scheduling, not lost edge"
        ),
        "decision_baseline": "next_open",
        "qualified_longs": len(qualified),
        "arms": [],
        "recovery": {},
    }
    _checkpoint(out_path, report)

    by_id: dict[str, dict] = {}
    for arm in PRE_REGISTERED_ARMS:
        if not args.quiet:
            print(f"running {arm['id']} ...", flush=True)
        windows = []
        for window_name, start, end in (
            ("train", None, validation_split),
            ("validation", validation_split, None),
            ("full", None, None),
        ):
            sim = bt._simulate_portfolio(
                qualified,
                prices,
                benchmark_closes,
                exit_policy,
                hold_days,
                ranking_key=ranking_key,
                max_positions=max_positions,
                risk_per_trade=risk_per_trade,
                atr_trail_multiplier=trail_multiplier,
                post_stop_reentry_fn=post_stop,
                start_date=start,
                end_date=end,
                fill_mode=str(arm["fill_mode"]),
                max_entry_gap_pct=arm.get("max_entry_gap_pct"),
                include_trades=True,
            )
            if sim is None:
                windows.append({"window": window_name, "error": "no_trades"})
                continue
            dsr = bt.deflated_sharpe_ratio(
                sim.get("sharpe"),
                sim.get("sharpe_se"),
                N_TRIALS,
                n_returns=sim.get("n_returns"),
                return_skew=sim.get("return_skew"),
                return_kurtosis=sim.get("return_kurtosis"),
            )
            sim.pop("trade_details", None)
            sim.pop("equity_curve", None)
            sim.pop("benchmark_curve", None)
            sim.pop("reentry_events", None)
            windows.append({"window": window_name, "dsr": dsr, **sim})
        row = {"id": arm["id"], "label": arm["label"], "config": {
            k: arm[k] for k in arm if k not in {"id", "label"}
        }, "windows": windows}
        by_id[arm["id"]] = row
        report["arms"].append(row)
        _checkpoint(out_path, report)
        if not args.quiet:
            val = _window(row, "validation") or {}
            print(
                f"  {arm['id']}: val Sharpe={val.get('sharpe')} "
                f"DD={val.get('max_drawdown_pct')} trades={val.get('trades')}",
                flush=True,
            )

    if all(k in by_id for k in ("close_control", "next_open", "stale_close")):
        report["recovery"] = _grade_stale_close(
            by_id["close_control"], by_id["next_open"], by_id["stale_close"]
        )
    _checkpoint(out_path, report)
    if not args.quiet:
        print(f"wrote {out_path}", flush=True)
        print(f"recovery: {report.get('recovery')}", flush=True)


if __name__ == "__main__":
    asyncio.run(_main())